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DTSTART;TZID=America/Los_Angeles:20260225T173000
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DTSTAMP:20260425T210153
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SUMMARY:Exploring Research Pathways at Baskin Engineering
DESCRIPTION:Curious how being part of a research lab can supercharge your experience as a Baskin Engineer?   \nJoin us for this informative event to learn about opportunities to solve open-ended problems\, build deeper technical skills\, and learn how to think like an engineer. \nWe’ll kick things off with a quick overview of the kinds of research opportunities available to undergrads and how to get started\, then you’ll hear directly from students who’ve worked in research labs as undergraduates. They’ll share what they actually did day-to-day\, the skills they built (technical and professional)\, and how research shaped their confidence\, career goals\, and next steps. We’ll then have pizza and networking to end the evening. \nWhether you’re aiming for industry\, graduate school\, or just want hands-on experience that goes beyond coursework\, this panel will help you understand how undergraduate research can set you apart—academically\, professionally\, and personally! \n\nRegister via Handshake. \nYOU BELONG HERE\nPrograms and services are open to all\, consistent with state and federal law\, as well as the University of California’s nondiscrimination policies. Every initiative—whether a student service\, faculty program\, or community event—is designed to be accessible\, inclusive\, and respectful of all identities. To learn more\, please visit UC Nondiscrimination Statement or Nondiscrimination Policy for UC Publications.
URL:https://events.ucsc.edu/event/exploring-research-pathways-at-baskin-engineering/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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DTSTART;TZID=America/Los_Angeles:20260225T110000
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SUMMARY:CSE Colloquium: Agile and evolvable software construction in the era of rapidly evolving hardware accelerator designs
DESCRIPTION:Presenter\n\nCharith Mendis\, Siebel School of Computing and Data Science\, University of Illinois at Urbana-Champaign\n\nAbstract\n\nModern AI workloads have become exceedingly abundant and important in the current computing landscape. As a result\, there have been numerous software and hardware innovations aimed at accelerating these workloads. However\, we observe a subtle disconnect between the software and hardware communities. Most software innovations target well-established hardware platforms such as CPUs (e.g.\, x86\, ARM) and GPUs (e.g.\, NVidia GPUs)\, while hardware innovations produce plenty of other tensor accelerator designs (e.g.\, Gemmini\, Feather\, Trainium) each year.\n\nWe asked the question\, why aren’t the software community using these accelerators or even evaluating on them? The simple yet undeniable reason is the lack of standardized software tooling compared to CPUs and GPUs. For an architecture to be used\, properly designed compiler backends\, correctness\, and performance testing tools should be abundant (e.g.\, CUDA ecosystem).\n\nIn this talk\, I will describe how we bridge this gap by automatically generating the necessary software tools for a large class of accelerators through the Accelerator Compiler Toolkit (ACT) ecosystem. Central to ACT is an ISA definition language\, TAIDL\, that for the first time standardizes the hardware-software interfaces for a large class of accelerators. Departing from the traditional approach of manually constructing test oracles\, performance models\, or retargetable compiler backends\, we instead introduce agile and evolvable methodologies to automatically generate such necessary tooling using both formal methods and machine learning techniques for any TAIDL-defined accelerator interface. I will show how such automation enables rapid software prototyping\, making rapidly evolving accelerator designs usable by the software community.\n\nBio\n\nCharith Mendis is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign. His broad research interests are at the intersection of compilers\, programming languages\, and machine learning. He received his Ph.D. and Master’s from the Massachusetts Institute of Technology and his B.Sc. from the University of Moratuwa. He is the recipient of the DARPA Young Faculty Award\, the NSF CAREER Award\, the Google ML and Systems Junior Faculty Award\, the Outstanding Advisor award at UIUC\, the William A. Martin Outstanding Master’s Thesis Award at MIT\, and the University Gold Medal for his B.Sc. He has won numerous paper awards\, including a Distinguished Paper Award at POPL\, a Best Student Paper Award at the IEEE BigData conference\, an honorable mention for the Best Artifact Award at SIGMOD\, a Best Paper Award at ML for Systems workshop at ISCA\, and an IEEE Top Picks Honorable Mention.\n\nHosted by: Professor Nikos Tziavelis\n\nLocation: Engineering 2\, E2-180 (Refreshments such as fruit\, pastries\, tea\, and coffee will be available for guests.)\n\nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3 
URL:https://events.ucsc.edu/event/cse-colloquium-agile-and-evolvable-software-construction-in-the-era-of-rapidly-evolving-hardware-accelerator-designs/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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DTSTART;TZID=America/Los_Angeles:20260225T090000
DTEND;TZID=America/Los_Angeles:20260225T120000
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SUMMARY:Liu\, C. (CSE) - Enabling LLM Unlearning at Inference Time by Decomposing Detection and Intervention
DESCRIPTION:Machine unlearning addresses the “right to be forgotten” under GDPR and enables privacy\, copyright\, and safety compliance in large language models. Training-based unlearning can remove targeted behavior on benchmarks\, but it scales poorly\, can degrade utility\, and can fail under adversarial prompting that recovers supposedly forgotten content. This prospectus proposes inference-time behavioral unlearning: rather than modifying weights to “erase” knowledge\, we detect when a query targets forgotten content and intervene in generation so the system behaves like a model never trained on that content. We formalize this approach as Detect-Intervene Decomposition and instantiate it with three complementary methods operating at the embedding\, token\, and reasoning levels under different access capabilities. Comprehensive experiments across entity unlearning\, hazardous knowledge removal\, and copyright protection demonstrate that our methods match or exceed training-based approaches while being orders of magnitude faster and preserving model utility. As LLMs increasingly operate as services with restricted weight access\, inference-time unlearning provides the only practical path for responsible AI deployment that respects privacy\, safety\, and legal requirements. \nEvent Host: Chris Liu\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Yang Liu \nZoom – https://ucsc.zoom.us/j/94799852992?pwd=EBFQe4U2lRNro1oJ8F36bgORhT2xSv.1 \nPasscode –  242384
URL:https://events.ucsc.edu/event/liu-c-cse-enabling-llm-unlearning-at-inference-time-by-decomposing-detection-and-intervention/
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260224T170000
DTEND;TZID=America/Los_Angeles:20260224T181500
DTSTAMP:20260425T210153
CREATED:20260130T054112Z
LAST-MODIFIED:20260209T231917Z
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SUMMARY:AI and Security 101
DESCRIPTION:Join us for an informative conversation with Neta Haiby\, Head of Product | AI Security at Microsoft! \nArtificial Intelligence is transforming both cyber defense and cyber offense. It creates unique risks in how we build\, deploy\, and operate AI apps and Agents. This session examines how AI can be attacked or misused – through techniques such as jailbreaks\, intent breaking\, and supply-chain compromise and discusses practical defense strategies\, including guardrails\, access controls\, monitoring\, and evaluation. \nDesigned for students interested in cybersecurity and AI\, this session emphasizes a practical understanding of AI security. \nAttendees will also receive resources to help them further explore and get started in the field! \nDon’t miss this highly informative event! \nYOU BELONG HERE\nPrograms and services are open to all\, consistent with state and federal law\, as well as the University of California’s nondiscrimination policies. Every initiative—whether a student service\, faculty program\, or community event—is designed to be accessible\, inclusive\, and respectful of all identities. To learn more\, please visit UC Nondiscrimination Statement or Nondiscrimination Policy for UC Publications.
URL:https://events.ucsc.edu/event/ai-and-security-101/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Undergraduate,Workshop
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260224T103000
DTEND;TZID=America/Los_Angeles:20260224T113000
DTSTAMP:20260425T210153
CREATED:20260129T145348Z
LAST-MODIFIED:20260209T232106Z
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SUMMARY:Transform Your Future Pop-Up (Cookies Included!)
DESCRIPTION:Join Baskin Engineering to celebrate National Engineers Week with a sweet stop at the Transform Your Future Pop-Up (Cookies Included!) 🍪☕ \nThis year’s Engineers Week theme\, Transform Your Future\, is a powerful reminder that engineering doesn’t just shape our world—it shapes our opportunities\, our communities\, and the futures we can imagine for ourselves. \nSwing by the BE Courtyard to grab cookies\, coffee\, and BE swag (first come\, first served!) and take a moment to celebrate how you are transforming your future. \n📅 Date: Tuesday\, February 24⏰ Time: 10:30 a.m.📍 Location: BE Courtyard \nWe hope to see you there!
URL:https://events.ucsc.edu/event/transform-your-future-pop-up-cookies-included/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Social Gathering,Undergraduate
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260220T140000
DTEND;TZID=America/Los_Angeles:20260220T160000
DTSTAMP:20260425T210153
CREATED:20260210T193542Z
LAST-MODIFIED:20260210T193542Z
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SUMMARY:Fredrickson\, K. (CSE) - Practical Anonymity with Formal Resistance to Traffic Analysis
DESCRIPTION:Anonymous communication systems hide who is talking to whom\, not just what is said. However\, existing systems are either vulnerable to traffic analysis attacks–attacks where adversaries observe and correlate the network traffic of users–or are forced to rely on unrealistic and unenforceable assumptions about how users behave. Worse\, existing theory lacks tools to rigorously model traffic analysis attacks\, much less inform whether if a system is secure against traffic analysis or how to design systems that are. \nWe make several contributions toward our goal of practical anonymity systems that resist traffic analysis. First\, we develop the first formal framework for describing the security of systems against traffic analysis attacks\, allowing us to quantitatively describe and compare the security of all existing works. Second\, leveraging this framework\, we develop a security definition that distinguishes between systems that are and are not susceptible to traffic analysis. We call this property input/output independence. We use this definition to prove that the dominant model of systems–synchronous systems–cannot practically provide input/output independence. We then design a new asynchronous anonymity functionality\, deferred retrieval\, that achieves input/output independence with far more flexible user assumptions and up to 3400 times less traffic overhead for the same latency compared to prior methods. Finally\, we design and implement Sparta\, a family of high-throughput\, scalable instantiations of deferred retrieval using trusted execution environments and oblivious algorithms\, yielding the first practical anonymity systems that are formally resistant to long-term traffic analysis. \nEvent Host: Kyle Fredrickson\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Darrell Long \nZoom – https://ucsc.zoom.us/j/98133127429?pwd=QNICsMrQa6bQUKNPo40PthZyQEQCFl.1 \nPasscode – 242206
URL:https://events.ucsc.edu/event/fredrickson-k-cse-practical-anonymity-with-formal-resistance-to-traffic-analysis/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260218T110000
DTEND;TZID=America/Los_Angeles:20260218T121500
DTSTAMP:20260425T210153
CREATED:20260210T212856Z
LAST-MODIFIED:20260210T212955Z
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SUMMARY:CSE Colloquium - Query Optimization: How to design a Meta-Algorithm that designs Algorithms?
DESCRIPTION:Presenter: Mahmoud Abo Khamis\, RelationalAI \nAbstract: \nDatabase systems have evolved from simple bookkeeping tools to comprehensive data analytics platforms capable of learning from the data and making business decisions. As a result\, database queries expanded in their expressive power and applications to include tensor computations\, constraint satisfaction problems\, graph analytics\, scientific computing\, SAT solving\, among others. This puts a lot of pressure on modern query optimizers to rise up to the occasion and produce efficient query plans for a wide variety of very complex queries that describe problems in different domains. The ultimate goal of query optimization is for the query optimizer to become a “meta-algorithm” where you can feed in any problem definition and get back an efficient algorithm for this particular problem. \nIn this talk\, we describe two related frameworks for query optimization that aim to take us one step in the direction of the above goal. The first framework is based on information theory. It uses information theory to get provably accurate cost estimates for query plans and to find the best query plan. Among other applications\, this framework currently achieves the best known complexity for graph pattern matching problems\, thus subsuming and generalizing known results in this area\, where\, for decades\, algorithms used to be designed by hand for specific graph patterns. The second framework is based on algebra. It uses algebraic abstractions to unify and generalize algorithms across different domains\, in the same way template programming allows for reusing code across different applications. \nBio: \nMahmoud Abo Khamis is a Senior Computer Scientist at RelationalAI\, where he has worked since 2017. He received his Ph.D. in Computer Science and Engineering from the State University of New York at Buffalo in 2016. Prior to joining RelationalAI\, he was a Senior Database Engineer at Infor from 2015 to 2017. His research interests include database systems and theory\, in-database machine learning\, query optimization and evaluation\, information theory\, and beyond worst-case analysis. His work has been recognized with two Test-of-Time Awards at ACM PODS 2025 and 2026\, three Best Paper Awards at ACM SIGMOD 2025 and ACM PODS 2022 and 2016\, three ACM SIGMOD Research Highlight Awards\, and the 2016 Best CSE Dissertation Award from SUNY Buffalo. His work has also received multiple invitations to the Journal of the ACM\, ACM STOC\, and ACM TODS. He is on the Editorial Board of ACM TODS\, and serves on the program committees of ACM PODS\, ICDT\, and ICALP among others. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, Room E2-180 (*Refreshments such as coffee\, tea\, pastries\, and fresh fruit will be provided in-person.) \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-query-optimization-how-to-design-a-meta-algorithm-that-designs-algorithms/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260211T110000
DTEND;TZID=America/Los_Angeles:20260211T121500
DTSTAMP:20260425T210153
CREATED:20260105T205936Z
LAST-MODIFIED:20260105T205936Z
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SUMMARY:CSE Colloquium: Incentivized Alignment for Strategic Agents (Human and Otherwise)
DESCRIPTION:Presenter: Grant Schoenebeck\, University of Michigan \nAbstract: Advances in machine learning enable new forms of human-AI collaboration\, but collaborative settings typically involve agents with divergent objectives and private information. This will become increasingly critical in the emerging world of agentic AI\, where ML-powered agents act on behalf of individuals or institutions with conflicting goals. I use the term incentivized alignment to describe the approach of combining both machine learning and incentive design to achieve alignment of system outcomes despite misaligned agents. This talk presents two case studies of incentivized alignment showing how machine learning can make mechanism design scalable and practical\, and how mechanism design can make machine learning strategically robust. First\, I examine the use of LLMs as judges for rating subjective responses. While LLMs perform well on existing datasets\, they are highly susceptible to manipulation. I propose adapting peer-prediction mechanisms to create strategically-robust scoring mechanisms that incentivize honest reporting. Beyond ensuring high-quality inputs to AI systems\, these mechanisms can potentially eliminate reward hacking in ML training pipelines. Second\, I consider collective decision-making where agents hold different objectives and private information. The goal is to design mechanisms that incentivize strategic agents to select outcomes that would be optimal under full information sharing\, according to certain criteria. Both case studies demonstrate solutions for incentivized alignment in multi-agent systems employing the combination of incentive design and machine learning\, a theme likely to be central to the future of collaborative AI. \nBio: Grant Schoenebeck is an associate professor at the University of Michigan in the School of Information. His work has recently focused on developing and analyzing systems for eliciting and aggregating information from a diverse group of agents with varying information\, interests\, and abilities by combining ideas from machine learning and economics (e.g. game theory\, mechanism design\, and information design). More generally\, his recent work has been about incentives and (machine) learning in a variety of contexts. His research is supported by multiple NSF grants including a CAREER award and spans publications in top venues including NeurIPS\, ICLR\, EC\, WINE\, the Web Conference\, STOC\, and FOCS. His former PhD students and postdocs now hold tenure-track positions at the University of Illinois Urbana-Champaign\, Peking University\, George Mason University\, and Shanghai Jiao Tong University. He recently served as Program Committee Co-chair for WINE\, Theory Track Co-chair for EC\, and Economics and Computation Track co-chair at the Web Conference. Grant received his PhD at UC Berkeley\, studied theology at Oxford University\, and received his BA in mathematics and computer science from Harvard. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, Room E2-180 \n*Light refreshments such as coffee\, pastries\, and fruit will be available. \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-incentivized-alignment-for-strategic-agents-human-and-otherwise/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260209T130000
DTEND;TZID=America/Los_Angeles:20260209T143000
DTSTAMP:20260425T210153
CREATED:20260127T195054Z
LAST-MODIFIED:20260127T195054Z
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SUMMARY:Li\, X. (CSE) - Compute-Efficient Scaling of Fully-Open Visual Encoders
DESCRIPTION:Vision encoders have demonstrated significant performance gains in visual generation and multimodal reasoning. These improvements are primarily attributed to the scaling of data\, model capacity\, and compute. However\, this progress is becoming less accessible due to a lack of transparency in data curation and training recipes. In combination with the high compute requirements of foundation-scale pre-training\, these factors hinder independent reproducibility. \nIn this dissertation\, we democratize large-scale visual encoder training by developing compute-efficient\, reproducible training recipes for video encoders\, vision-language models (VLMs)\, and multimodal large language models (MLLMs). First\, we challenge the common belief that scaling necessarily requires proportionally more resources. Specifically\, we show that decoupled pre-training separates key factors such as space/time and token length\, and learns strong priors first. This design yields dramatic efficiency gains across image\, video\, and generative modeling. Next\, we address the challenge of undisclosed or inaccessible training data by releasing and systematically studying the curation of high-quality\, large-scale datasets. We demonstrate that high-quality synthetic captions at scale enable vision-language models to learn stronger visual representations\, especially when paired with training frameworks that unify contrastive and generative objectives. Lastly\, building on these findings\, we develop fully open vision encoders with complete training data\, recipes\, and checkpoints\, and show that transparency can enable rather than hinder state-of-the-art performance as an MLLMs’ visual backbone. \nTogether\, these contributions establish that openness and efficiency are mutually reinforcing\, providing a reproducible foundation for the next generation of visual intelligence. \nEvent Host: Xianhang Li\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Cihang Xie  \nZoom- https://ucsc.zoom.us/j/95801462664?pwd=koENnyV65jyPnkJYTbiYr1jaNsV5BE.1 \nPasscode- 782017
URL:https://events.ucsc.edu/event/li-x-cse-compute-efficient-scaling-of-fully-open-visual-encoders/
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260206T160000
DTEND;TZID=America/Los_Angeles:20260206T180000
DTSTAMP:20260425T210153
CREATED:20260128T172826Z
LAST-MODIFIED:20260128T172826Z
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SUMMARY:Yang\, J. (CSE) - Towards Controllable and Compositional Generative Vision
DESCRIPTION:Diffusion-based text-to-image models can generate impressive images\, but they largely treat an image as a single\, flat output\, which makes precise editing of individual elements difficult. This proposal studies layered generative representations that align with professional editing workflows\, enabling users to manipulate foreground objects while preserving the rest of the scene. A central focus is visual effects such as shadows and reflections\, which are essential for realistic composition yet are often missing or inconsistent in current generative pipelines. This proposal outlines a research program toward controllable\, compositional image generation that supports practical\, edit-ready content creation. \nEvent Host: Jinrui Yang\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Yuyin Zhou \nZoom- https://ucsc.zoom.us/j/91510964517?pwd=NG5Urv2li9HxlcUKrybg6Z5ZtYj9e6.1 \nPasscode- 544143
URL:https://events.ucsc.edu/event/yang-j-cse-towards-controllable-and-compositional-generative-vision/
CATEGORIES:Ph.D. Presentations
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LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260128T110000
DTEND;TZID=America/Los_Angeles:20260128T121500
DTSTAMP:20260425T210153
CREATED:20260120T191337Z
LAST-MODIFIED:20260120T191337Z
UID:10008678-1769598000-1769602500@events.ucsc.edu
SUMMARY:CSE Colloquium - Towards Relational Foundation Models: Zero-Shot Forecasting over Relational Databases
DESCRIPTION:Presenter: Charilaos I. Kanatsoulis\, Stanford University \nAbstract: Foundation models have transformed unstructured domains such as language and vision\, yet relational datasets\, where most enterprise knowledge lives\, still rely on brittle\, task-specific ML pipelines. I will begin by introducing Relational Deep Learning (RDL)\, a general framework for learning directly from heterogeneous multi-table data\, capturing structure across entities\, attributes\, and relationships without handcrafted schemas or features. \nBuilding on this paradigm\, I will present the Relational Transformer (RT)\, a schema-invariant model pretrained across diverse relational databases that performs structural learning with in-context information and transfers zero-shot to new databases and predictive tasks. By modeling both inter- and intra-table dependencies and reframing prediction as pattern recognition inside a unified latent relational space\, RT represents a concrete step toward relational foundation models that can be prompted\, reused\, and generalized for new problems. \nBio: Charilaos I. Kanatsoulis is a Research Scientist in the Department of Computer Science at Stanford University. He previously was a Postdoctoral Researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania and received his Ph.D. in Electrical and Computer Engineering from the University of Minnesota\, Twin Cities. His research lies at the intersection of machine learning and signal processing\, with a focus on Transformer and foundation model design for structured data\, graph representation learning\, tensor analysis\, and explainable AI. His work has been recognized with the Best Paper Award at the KDD Temporal Graph Learning Workshop (2025) and the Best Student Paper Award at IEEE CAMSAP (2023). He co-instructs CS246 and CS224W at Stanford and previously taught ESE 5140 at Penn. He has organized several community events\, including the Graph Signal Processing short course at IEEE ICASSP 2023\, the Stanford Graph Learning Workshop (2024–2025)\, the Relational Deep Learning tutorial at ACM KDD 2025\, and the New Perspectives in Advancing Graph Machine Learning Workshop at NeurIPS 2025. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, Room E2-180 (Refreshments such as coffee\, pastries\, and fruit will be provided.) \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-towards-relational-foundation-models-zero-shot-forecasting-over-relational-databases/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260123T093000
DTEND;TZID=America/Los_Angeles:20260123T110000
DTSTAMP:20260425T210153
CREATED:20260120T223725Z
LAST-MODIFIED:20260120T223725Z
UID:10008684-1769160600-1769166000@events.ucsc.edu
SUMMARY:Sharma\, R. (CSE) - Automatically Evolving GPU Libraries for Performance Portable AI Kernels
DESCRIPTION:GPUs are the workhorses of modern AI\, widely deployed and developed by many vendors including Apple\, Qualcomm\, Intel\, AMD\, and NVIDIA. While these GPUs all offer high compute potential\, programming them effectively is difficult because they differ in performance-critical features like SIMT width\, cache capacity\, and memory bandwidth\, demanding different optimization strategies. Tunable kernels address this by exposing parameters such as tiling dimensions and workgroup sizes\, enabling per-device specialization. Yet this produces static libraries: tuned once\, then frozen\, degrading as new hardware emerges. We propose automatically evolving libraries that expand their tuning knowledge as new hardware emerges\, with minimal impact on user experience. \nTo build such libraries\, we first need to understand the tuning landscape. We address this through GPU Goldmines\, a WebGPU-based framework for exhaustively collecting tuning data across diverse devices. Our tuned matrix multiplication kernels outperform an optimized baseline by 8.4x on average\, while matrix-vector kernels achieve 93% of platform bandwidth. We find that hyper-tuning for a single GPU causes 50% performance degradation on other devices\, whereas data-driven portability methods recover 88% of peak performance. These kernels are fundamental to the prefill and decode phases of LLM inference. We integrate them into llama.cpp as our evaluation platform\, where they outperform CPU and Vulkan backends. \nBuilding on this data\, we are developing Living Libraries to improve performance continuously without disrupting users. This means choosing good parameters upfront\, learning from real-world execution\, and knowing when to keep searching versus when to stop\, though hand-designed parameter spaces remain inherently bounded. To move beyond this\, we extend toward LLM-based kernel evolution\, where language models propose entirely new kernel variants\, opening a less structured but higher potential search space. \nEvent Host: Rithik Sharma\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Tyler Sorensen & Yuanchao Xu   \n  \nZoom: https://ucsc.zoom.us/j/92739836317?pwd=0ydDzimUFIoaLDUKst96dk27th4lvW.1 \nPasscode: 089560
URL:https://events.ucsc.edu/event/sharma-r-cse-automatically-evolving-gpu-libraries-for-performance-portable-ai-kernels/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260121T110000
DTEND;TZID=America/Los_Angeles:20260121T123000
DTSTAMP:20260425T210153
CREATED:20260105T203936Z
LAST-MODIFIED:20260105T205329Z
UID:10008262-1768993200-1768998600@events.ucsc.edu
SUMMARY:CSE Colloquium - Constraining Chaos: Toward Faithful and Semantic Decoding in Language Models
DESCRIPTION:Presenter: Loris D’Antoni\, UC San Diego \nAbstract:\nLanguage models excel at producing fluent text\, but in domains like code and math\, fluency isn’t enough — outputs must obey strict syntactic and semantic rules. A new wave of research is rethinking decoding itself: not as a process of sampling words\, but as a negotiation between probability\, structure\, and meaning. In this talk\, I’ll explore how grammar and semantics can be embedded into the decoding loop\, how we can sample from the true model conditional distribution under constraints\, and how programmable abstractions make it possible to enforce properties like type safety or program invariants. The result is a vision of decoding that is faithful to the model yet governed by rules\, pointing toward a future where LLMs generate not just plausible text\, but reliably correct output. \nBio:\nLoris D’Antoni is a Jacobs Faculty Scholar and Associate Professor in the Department of Computer Science and Engineering at the University of California San Diego. His research helps people build trustworthy software. His work has introduced new frameworks for verifying and synthesizing programs—ranging from resilient network configurations to robust decision-making systems—and\, more recently\, methods for aligning language models with user intent. \nHe is the recipient of an NSF CAREER Award and a Microsoft Research Faculty Fellowship\, and was selected as a Vilas Associate at the University of Wisconsin-Madison. He has also received Google\, Amazon\, and Meta Faculty Awards\, and the Morris and Dorothy Rubinoff Dissertation Award. His papers have earned several best paper awards and nominations\, including at TACAS\, ESOP\, ICDCN\, and SBES. \nLoris received his B.S. and M.S. in Computer Science from the University of Torino\, and his Ph.D. in Computer Science from the University of Pennsylvania. Before joining UC San Diego\, he was a faculty member at the University of Wisconsin–Madison. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, Room E2-180 \n*Light refreshments such as coffee\, pastries\, and fruit will be available. \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-constraining-chaos-toward-faithful-and-semantic-decoding-in-language-models/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260112T170000
DTEND;TZID=America/Los_Angeles:20260112T183000
DTSTAMP:20260425T210153
CREATED:20251209T200526Z
LAST-MODIFIED:20251218T001742Z
UID:10005751-1768237200-1768242600@events.ucsc.edu
SUMMARY:Be Inspired: Explore Graduate Studies in STEM
DESCRIPTION:Not sure if graduate school is right for you? \nJoin us to learn what graduate school is really about and explore whether it’s the right path for you. We’ll cover topics such as qualifying exams\, funding options\, common misconceptions\, and more! \nClick the link below to register for the event: \nhttps://ucsc.zoom.us/webinar/register/WN_31OHhwc7QPqJ7nSyiuAUNg
URL:https://events.ucsc.edu/event/be-inspired-explore-graduate-studies-in-stem/
CATEGORIES:Seminars,Workshop
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2025/12/Graduate-Student-Workshop-Flyer.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251208T140000
DTEND;TZID=America/Los_Angeles:20251208T150000
DTSTAMP:20260425T210153
CREATED:20251205T175704Z
LAST-MODIFIED:20251205T175952Z
UID:10005750-1765202400-1765206000@events.ucsc.edu
SUMMARY:Wang\, Y. (CSE) - Toward Practical and Effective Large Language Model Unlearning
DESCRIPTION:The growing integration of Large Language Models (LLMs) into real-world applications has heightened concerns about their trustworthiness\, as models may reveal private information\, reproduce copyrighted content\, propagate biases\, or generate harmful instructions. These risks\, alongside emerging privacy regulations\, motivate the need for LLM unlearning\, methods that remove the influence of specific data while preserving overall model capability.\nThis proposal investigates how to design practical and effective unlearning methods that enable LLMs to produce reliable and responsible outputs. We study both training-free and training-based paradigms. On the training-free side\, we introduce ECO\, which achieves unlearning via embedding-corrupted prompts detected by a lightweight classifier\, and DRAGON\, a generalizable black-box framework that combines detection with chain-of-thought guard reasoning for safe in-context intervention. On the training-based side\, we present FLAT\, a forget-data-only loss adjustment method grounded in a variational $f$-divergence formulation.\nTogether\, these approaches provide complementary strategies for aligning LLM behavior with safety and regulatory requirements while maintaining general utility. This proposal outlines their motivation\, design\, empirical performance\, and the broader research plan toward responsible and accountable LLM systems. \nHost: Yaxuan Wang\, Ph.D. Student\, Computer Science and Engineering  \nAdvisor: Yang Liu \nZoom- https://ucsc.zoom.us/j/94186242839?pwd=ubGMNF25W8gABNIl2S7EaIBHEXletV.1 \nPasscode- 786334
URL:https://events.ucsc.edu/event/wang-y-cse-toward-practical-and-effective-large-language-model-unlearning/
CATEGORIES:Ph.D. Presentations
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LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251208T130000
DTEND;TZID=America/Los_Angeles:20251208T140000
DTSTAMP:20260425T210153
CREATED:20251203T220535Z
LAST-MODIFIED:20251203T220535Z
UID:10005728-1765198800-1765202400@events.ucsc.edu
SUMMARY:Ferdous\, N. (CSE) - SPECSIM : A Simulation Infrastructure Mitigating Transient Timing Attacks
DESCRIPTION:   Transient execution attacks are serious security threats in modern-day processors. Out-of-order execution compels the processor to access data that should not be otherwise perceived. Leakage of that secret information creates a covert channel for the attacker for various types of transient and speculative attacks. Transient based execution attacks emanate when the secret information is leaked by the execution of transient instructions which are executed by the processor but never got committed from the processor pipeline. However\, on the microarchitectural level\, the effect of these transient instructions is noticeable. Generally\, microarchitectural state is the state that a processor maintains to improve performance which is transparent to software. The secret data retained in the microarchitectural state are susceptible to create a covert channel and thereby are at higher risk to be observed by the attacker for transient attacks.\nThis research work presents a robust and secure simulation infrastructure that implements multiple strategies to mitigate transient attacks in the timing domain. This work proposes various strategies e.g.\, Reorder Buffer Transient Flushing Technique in Randomized Transient Pipeline\, SpecSCB for making the speculative instructions invisible to the architectural state\, for the mitigation of the timing attack. In this work\, transient instructions are added in the proposed Randomized Transient Pipeline and are flushed effectively\, using Transient Flushing Techniques\, squashing all the transient instruction residues that could remain in the Randomized Transient Pipeline. This flushing strategy also ensures no difference in the execution time of the base simulation and the proposed Randomized Transient Simulation\, leaving no leakage for transient based timing attacks. In addition to the simulation platform\, a novel Transient Verification Framework is also proposed which consists of Global Time Signature Verification Model and Retirement Time Signature Verification Model. The transient verification framework identifies if there is any anomaly in the timing domain\, related to all existing instructions\, which could leave space for covert channel for timing attacks. Overall\, this work has provided an extensive and robust simulation platform infrastructure for the researchers to explore various types of attacks with their respective mitigating solutions. \nHost: Nilufar Ferdous\, Ph.D. Student\, Computer Science and Engineering  \nAdvisor: Jose Renau  \nZoom- https://us06web.zoom.us/j/84111701472?pwd=l3s5sQszKt35paVOWNxxLaE8jphG80.1 \nPasscode- Qi1pAk
URL:https://events.ucsc.edu/event/ferdous-n-cse-specsim-a-simulation-infrastructure-mitigating-transient-timing-attacks/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251208T093000
DTEND;TZID=America/Los_Angeles:20251208T104500
DTSTAMP:20260425T210153
CREATED:20251117T202808Z
LAST-MODIFIED:20251119T192125Z
UID:10005162-1765186200-1765190700@events.ucsc.edu
SUMMARY:CSE Colloquium: Making Systems Secure with Information Flow
DESCRIPTION:Presenter: Andrew Myers\, Cornell University\n\nAbstract:\nModern civilization depends on complex\, interconnected software systems that must safeguard trustworthy or private data. We have ever-growing mountains of code yet lack principled ways to build large systems that are secure. What is missing is a way to securely build these systems compositionally: module by module and layer by layer. Information flow control\, enforced throughout software and hardware\, offers a plausible way to achieve compositional security\, and is increasingly being used by industry. I describe how my research group has incorporated information-flow security into various languages and systems: hardware architectures resilient to timing and speculation attacks\, smart contracts\, and automatically synthesized cryptographic and distributed protocols. Information flow is inherently compositional and makes possible strong\, provable security guarantees that can be connected to cryptographic security definitions. Importantly\, it also guides developers during the design process\, exposing security-critical decisions up front. \nBio:\nAndrew Myers is the Class of 1912 Professor of Engineering in the Department of Computer Science at Cornell University. He received his Ph.D. in Electrical Engineering and Computer Science from MIT\, advised by Barbara Liskov. His research interests include programming languages\, computer security\, and distributed and persistent programming systems. His work on computer security has focused on practical\, sound\, expressive languages and systems for enforcing information security. Myers is an ACM Fellow and has authored several award-winning papers. He currently serves as the chair of the ACM SIGPLAN Executive Committee. \nHosted By: Professor Mohsen Lesani \nLocation: Engineering 2\, E2-180 \nZoom: https://ucsc.zoom.us/j/97682837116?pwd=WZBzhJY4p7rTZshqglmOs6xBtBasbE.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-making-systems-secure-with-information-flow/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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GEO:37.0009723;-122.0632371
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251208T091500
DTEND;TZID=America/Los_Angeles:20251208T103000
DTSTAMP:20260425T210153
CREATED:20251205T173457Z
LAST-MODIFIED:20251205T174005Z
UID:10005749-1765185300-1765189800@events.ucsc.edu
SUMMARY:Jamilan\, S. (CSE) -  Profile-guided Compiler Optimizations for Data Center Workloads
DESCRIPTION:Modern applications\, such as data center workloads\, have become increasingly complex. These applications primarily operate on massive datasets\, which involve large memory footprints\, irregular access patterns\, and complex control and data flows. The processor-memory speed gap\, combined with these complexities\, can lead to unexpected performance inefficiencies in these applications\, preventing them from achieving optimal performance. Considering the complexity and size of data center applications\, manually identifying and resolving performance issues is often impractical or impossible. Instead\, developing new compiler optimization techniques can be a more effective and scalable solution to boost both performance and energy efficiency. In this thesis\, we focus on identifying the root causes that limit the performance of data center workloads. We analyze the limitations of current profile-guided compiler optimization techniques for addressing these performance gaps. Finally\, we propose two profile-guided optimization techniques\, APT-GET and RIFS\, which can be integrated into the LLVM optimization pipeline to deliver further improvements. To hide the long latency of memory accesses\, we introduce APT-GET\, a profile-guided technique that ensures timely prefetches by leveraging dynamic execution-time information to build a novel analytical model that finds the optimal prefetch distance and injection site based on the collected profile. We study APT-GET across 10 real-world applications and demonstrate that it achieves a speedup of up to 1.98× and an average of 1.30×. To enable runtime value-invariant function specialization to reduce redundant operations\, we introduce RIFS\, a profile-guided compiler technique that specializes functions based on runtime-invariant call-site-specific argument values. RIFS introduces a novel value-profiling LLVM pass to identify runtime invariant arguments and a subsequent LLVM transformation pass to generate specialized function variants tailored to these value profiles. To efficiently select among potentially thousands of specialization candidates\, we develop a predictive cost model that estimates each candidate’s performance benefit before code generation. RIFS achieves an average speedup of 5.3% and an instruction reduction of 2.5% over the LLVM -O3+PGO baseline across 12 real-world applications. \nHost: Saba Jamilan\, Ph.D. Candidate\, Computer Science and Engineering  \nAdvisor: Heiner Litz  \nZoom- https://ucsc.zoom.us/j/95818759324?pwd=rdaS7G1V7O6faRhNOgFyq1OR50eSLK.1 \nPasscode- 652917 \n 
URL:https://events.ucsc.edu/event/jamilan-s-cse-profile-guided-compiler-optimizations-for-data-center-workloads/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251205T130000
DTEND;TZID=America/Los_Angeles:20251205T140000
DTSTAMP:20260425T210153
CREATED:20251203T234430Z
LAST-MODIFIED:20251203T234430Z
UID:10005731-1764939600-1764943200@events.ucsc.edu
SUMMARY:Garg\, S. (CSE) - MAPPING ANNOTATIONS FROM NETLIST TO SOURCE CODE
DESCRIPTION:Hardware design flows have become increasingly complex as modern chips integrate billions\nof transistors and rely on aggressive synthesis optimizations to meet performance\,\narea\, and power targets. While these transformations improve circuit efficiency\, they\nalso erase the correspondence between gate-level netlists and their originating HDL\nsource lines. The loss of traceability makes post-synthesis debugging\, timing backannotation\,\nand root-cause analysis extremely difficult. Existing solutions depend on\ntool-specific metadata or preserved signal names\, which are often lost after flattening\,\nretiming\, or logic restructuring.\nTo address this long-standing problem\, this thesis presents SynAlign\, a structural\nalignment framework that restores the mapping between optimized netlists and\nsource code without relying on synthesis metadata. SynAlign treats both the reference\nRTL and synthesized designs as graphs and iteratively aligns them using shared\nstructural cues—such as sequential boundaries\, fan-in/fan-out relationships\, and partial\nnaming patterns. The algorithm employs anchor-based seeding\, multi-stage neighborhood\nmatching\, and a lightweight scoring function to propagate correspondences\nefficiently across large designs.\nExtensive evaluation demonstrates that SynAlign achieves over 90% line-level\nalignment accuracy across diverse designs\, maintaining robustness even when 60% of\nsignal names are obfuscated or removed. The framework scales linearly with design size\,\ncompleting alignment on multi-million-node circuits within minutes. Controlled tests\nconfirmed structural stability under synthetic noise\, while production-level validation\non real processor and accelerator modules verified industrial applicability.\nBy recovering structural visibility lost during synthesis\, SynAlign bridges a\ncritical gap between front-end design intent and post-synthesis implementation. Its explainable\nalignment enables faster debug cycles\, more accurate timing correlation\, and\nprovides a foundation for next-generation EDA tools that integrate traceability\, optimization\ntransparency\, and source-level introspection into the hardware development\nprocess. \nHost: Sakshi Garg\, Ph.D. Candidate\, Computer Science and Engineering  \nAdvisor: Jose Renau \nZoom- https://ucsc.zoom.us/j/96207792766?pwd=bjBfusfaucoqMGZNgayum2te4tsLc5.1 \nPasscode- 669162
URL:https://events.ucsc.edu/event/garg-s-cse-mapping-annotations-from-netlist-to-source-code/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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GEO:37.0009723;-122.0632371
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251205T090000
DTEND;TZID=America/Los_Angeles:20251205T110000
DTSTAMP:20260425T210153
CREATED:20251118T165217Z
LAST-MODIFIED:20251119T192149Z
UID:10005180-1764925200-1764932400@events.ucsc.edu
SUMMARY:Littschwager\, N. (CSE) - A Proposal for Characterizing Replicated Systems and Emulators
DESCRIPTION:Simulation is a coinductive proof technique to assert the behavioral equivalence of computing systems that has seen fruitful application in distributed systems\, concurrent process calculi\, and programming languages\, since the 1970’s. We have also utilized simulation in our prior work\, where we formalized and proved a folklore claim that the state-based and operation-based approaches to Conflict-free Replicated Data Types (CRDTs) are ‘equivalent’ since they can ‘emulate each other’. More specifically\, a CRDT system consists of a collection of nodes called replicas. Clients interact with individual replicas by querying or updating their state\, and replicas interact by message passing over a network to eventually reach a convergent state. There are two main approaches to implementing a CRDT: operation-based\, and state-based. We showed that the main state-based and operation-based approaches to CRDTs do indeed ‘emulate each other’ since one can exhibit a pair of weak simulations between the original type of CRDT\, and its corresponding translation into the other type. We then leveraged the existence of these weak simulations to formally prove a ‘representation independence’ result\, in the sense that when access to the CRDTs is mediated by an imperative programming language\, the programmer cannot discern the underlying CRDT implementation by producing a program that terminates when run using one type of CRDT implementation\, but not when run with the other. \n Unfortunately\, our results are impractical for the purpose of being reapplied to asserting the equivalence of other replicated systems\, since the simulation relations (that one needs to exhibit in order to prove the necessary representation-independence) are non-modular\, requiring the user to reason about the potential executions of their entire replicated system. Additionally\, we observed that behavioral equivalence of state-based and operation-based CRDTs is a specific instance of the more general paradigm of ‘emulation’\, which is the process by which an ‘emulator’ translates the behavior of one system into the behavior of a different system. \nWe propose to generalize the techniques of our prior work to be applicable for any pair of replicated    systems\, and correct the ‘non-modularity’ issue by decomposing the overall proof structure into compositional simulation proofs about the local behavior of a replica\, and the behavior of the communication medium. Our second proposal comes from the observation that\, to our knowledge\, ‘emulation’ has not been given a formal and general mathematical semantic model that adequately captures the practical nuances faced by researchers and practitioners working on emulators. With that in mind\, we propose a notion of a faithful emulator\, inspired by the concept of a faithful functor 𝐹 ∶ C → D which lets us regard objects in C as ‘the same as’ the objects in D\, but with additional structure. \nHost: Nathan Littschwager\, Ph.D. Student\, Computer Science and Engineering  \nAdvisor: Lindsey Kuper  \n 
URL:https://events.ucsc.edu/event/littschwager-n-cse-a-proposal-for-characterizing-replicated-systems-and-emulators/
CATEGORIES:Ph.D. Presentations
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LOCATION:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251204T130000
DTEND;TZID=America/Los_Angeles:20251204T140000
DTSTAMP:20260425T210153
CREATED:20251119T191957Z
LAST-MODIFIED:20251119T191957Z
UID:10005182-1764853200-1764856800@events.ucsc.edu
SUMMARY:GradWiC Womxn's Lunch
DESCRIPTION:Join Graduate Womxn in Computing (GradWiC) for our final Womxn’s Luncheon of the quarter. We will be on the E2 Lanai patio weather allowing\, or E2-599 in the case of inclement weather.
URL:https://events.ucsc.edu/event/gradwic-womxns-lunch/
CATEGORIES:Social Gathering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251203T110000
DTEND;TZID=America/Los_Angeles:20251203T123000
DTSTAMP:20260425T210153
CREATED:20251103T224713Z
LAST-MODIFIED:20251119T191907Z
UID:10005028-1764759600-1764765000@events.ucsc.edu
SUMMARY:When Less is More: Applications of Type-Based Underapproximate Reasoning
DESCRIPTION:Presenter: Suresh Jagganathan\, Purdue University\n\n\nAbstract:\nUnlike program verifiers\, symbolic execution and property-based testing tools underapproximate program behavior: they aim to report only real bugs (no false positives)\, at the cost of potentially missing some (false negatives). Recent work has sought to place such tools on a more formal footing\, primarily through the development of incorrectness logics that capture a program’s ‘must’ rather than ‘may’ behavior. This talk explores how to transplant these ideas of underapproximation into an expressive refinement type system. Our development enables us to:\n\n(a) Typecheck the completeness of property-based testing (PBT) generators\, ensuring that a well-typed generator produces all values (i.e.\, fully covers) its output type;\n\n(b) Synthesize effectful generators by extending the type system to model underapproximations of sequences of effects rather than just values; and\n\n(c) Guide symbolic execution in effectful functional programs\, prioritizing execution paths capable of falsifying data structure safety properties.\n\nOur results demonstrate that viewing types through the lens of underapproximation offers a principled foundation for designing\, implementing\, and reasoning about program analyzers and test generators\, significantly improving their reliability and practical utility in the process.\n\n\nBio:\nSuresh Jagannathan is the Samuel D. Conte Professor of Computer Science at Purdue University. His interests span functional programming\, program verification\, distributed and concurrent systems\, and trustworthy machine learning. In recent years\, he has spent time as an Amazon Scholar\, a program manager at the Information Innovoation Office (I2O) at DARPA\, and a visiting researcher at the University of Cambridge. He serves an Associate Editor of ACM TOPLAS\, and has served as both General and PC Chair of POPL (ACM Symposium on Programming Languages).\n\n\nHosted by: Professor Mohsen Lesani\n\n\nLocation: E2-180\n*Refreshments such as coffee and pastries will be provided\n\n\n\nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/when-less-is-more-applications-of-type-based-underapproximate-reasoning/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20251124T093000
DTEND;TZID=America/Los_Angeles:20251124T113000
DTSTAMP:20260425T210153
CREATED:20251112T181924Z
LAST-MODIFIED:20251112T181924Z
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SUMMARY:Chen\, Q. (CSE) - New Approximation and Online Algorithms using Novel Combinatorial Structures
DESCRIPTION:Most optimization problems face the challenge of computing an optimum solution requiring superpolynomial time. In particular\, they are classified as NP-hard problems that have no polynomial-time algorithm to date. Instead\, computer scientists turn to find an approximate solution and create numerous elegant algorithms. However\, in the modern era\, computational environments have changed drastically\, and we are not able to afford to design new algorithms for each new problem via repeated trial and error. Therefore\, systematic ways to understand the possibilities and limitations of these problems are desired. This dissertation studies several central combinatorial optimization problems\, focusing on understanding the key structural obstacles and developing unified frameworks. Mainly\, we study two types of combinatorial optimization problems:\n(1) Scheduling. The problem is associated with limited resources\, and our target is to find an allocation method to complete all jobs over time that minimizes the overall budget cost.\n(2) Network Design. Different from scheduling problems. In this problem\, we aim to find a minimum-cost topological network that supports routing for demanding communications. \nOur first work is focused on a group-to-group survivable network design problem that generalizes the classic point-to-point network to support routing between any pair of subsets of nodes. Previous research stops at limited faults\, and the difficulty comes from the way to compress the graph into a tree. We propose a new framework via capacitated tree embeddings against arbitrary faults in the network\, which gives the first polylogarithmic approximation algorithm. Further\, this framework captures nearly all the recent models proposed in the area. \nIn contrast to the offline optimization problems mentioned above\, online algorithms are natural adaptations that have been found in tremendous real applications. In online algorithms\, the algorithm wants to compete against arbitrary uncertainty\, which means the instance is unknown at first and revealed over time. We study various scheduling problems and focus on some important metrics – average flow time\, which measures the average time a job stays in the system from its arrival to completion. Real-world demands give online scheduling problems enormously different settings. Computer scientists need to repeat errors and trials to find a provably good solution. We find the key required combinatorial property is supermodularity for the residual objective\, which measures the average completion time for all alive jobs assuming they have the same arrival time. Further\, we relate supermodularity with gross-substitute/linear-substitute (GS/LS)\, which is a well-studied definition in economics. Finally\, we propose a meta-algorithm that solves all captured problems in one shot. \nEvent Host: Qingyun Chen\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Sungjin Im \nZoom-  https://ucsc.zoom.us/j/94376536164?pwd=cPloEcyKuQg1C9reIbuh6rejrOaRfR.1
URL:https://events.ucsc.edu/event/chen-q-cse-new-approximation-and-online-algorithms-using-novel-combinatorial-structures/
CATEGORIES:Ph.D. Presentations
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LOCATION:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251120T090000
DTEND;TZID=America/Los_Angeles:20251120T100000
DTSTAMP:20260425T210153
CREATED:20251118T162058Z
LAST-MODIFIED:20251118T162058Z
UID:10005178-1763629200-1763632800@events.ucsc.edu
SUMMARY:Jorquera\, Z. (CSE) - Quantum Entanglement Bounds and the Approximation Algorithms That Use Them
DESCRIPTION:One of the central challenges in quantum computing is finding or approximating the ground-state energy of a local Hamiltonian\, a quantum analogue of classical constraint satisfaction problems (CSPs). Among these\, the Quantum Max-Cut problem serves as a canonical example\, paralleling the classical Max-Cut problem. Despite its foundational importance in both theoretical computer science and condensed matter physics\, our understanding of approximation algorithms for Quantum Max-Cut and related local Hamiltonian problems remains limited\, primarily due to the difficulty of representing and optimizing over entangled quantum states. \nIn this advancement talk\, we introduce the quantum information background needed to contextualize the results and the significance of the proposed future work by drawing an analogy to classical optimization. We then investigate approximation algorithms for 2-local Hamiltonians beyond qubit systems\, focusing on higher-dimensional qudit analogues\, such as Quantum Max-d-Cut and a new problem we introduce: the Maximal Entanglement problem. We establish new entanglement upper bounds for these problems based on the star bound\, a key tool for analyzing entanglement monogamy in Hamiltonian optimization. For the Maximal Entanglement problem\, we show that these bounds can be efficiently certified via semidefinite programs (SDPs) and that they directly admit a (1/d + O(1/D))-approximation algorithm (where D is the degree of the interaction graph)\, which beats random assignment. For Quantum Max-d-Cut\, the star bound gives a more complicated notion of entanglement\, for which we show that the basic SDP can verify this bound for all reduced marginals on up to five vertices when d=3\, but likely fails for larger subgraphs. We further propose that b-matchings\, with b = d-1\, capture the appropriate notion of entanglement for these higher-dimensional Quantum Max-d-Cut systems\, analogous to matchings in the qubit/Quantum Max-Cut case. Leveraging this insight\, we design a novel 2-matching-based algorithm that outperforms existing approaches for Quantum Max-3-Cut\, giving an approximation ratio of 0.555. \nThe present work advances the theoretical framework for understanding approximations in qudit Hamiltonians and highlights open directions for certifying quantum upper bounds as well as finding lower bounds via approximation algorithms. \n  \nEvent Host: Zack Jorquera\, Ph.D. Student\, Computer Science and Engineering  \nAdvisor: Alexandra Kolla  \nZoom- https://ucsc.zoom.us/j/98034235739?pwd=k260nd9labWT8xoQ9Cv3m2TATGw7VB.1
URL:https://events.ucsc.edu/event/jorquera-z-cse-quantum-entanglement-bounds-and-the-approximation-algorithms-that-use-them/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251119T110000
DTEND;TZID=America/Los_Angeles:20251119T121500
DTSTAMP:20260425T210153
CREATED:20251105T220936Z
LAST-MODIFIED:20251118T181912Z
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SUMMARY:CSE Colloquium - Flux: Refinement Types for Verified Rust Systems
DESCRIPTION:Presenter: Ranjit Jhala\, UCSD\n\nAbstract: Rust has risen as a language of choice for new systems code — from OS kernels to hypervisors\, firmware and run-times — as it is memory safe and provides the sort of abstractions needed for efficient low-level systems implementation. We present Flux\, a refinement type checker for Rust that shows how logical refinements can work in tandem with Rust’s ownership mechanisms to yield ergonomic type-based verification of low-level systems code. We then present a case study showing how Flux was used to formally verify process isolation in Tock: a microcontroller OS used in security-critical systems like the Google Security Chip (GSC) and Microsoft’s Pluton security processor. Our verification effort unearthed multiple subtle bugs that broke isolation\, allowing malicious applications to compromise the OS to potentially steal sensitive data or brick or take control of the OS. We describe how Flux helped design and implement a new granular process abstraction that is both simpler\, more efficient\, and yields formally verified security guarantees.\n\nBio:\nRanjit Jhala is a professor of Computer Science and Engineering at the University of California\, San Diego. He works on algorithms and tools that help engineer reliable computer systems. His work draws from and contributes to the areas of Model Checking\, Program Analysis\, and Automated Deduction\, and Type Systems. He helped create several influential and award winning systems including the BLAST software model checker and Liquid Types\, received ACM SIGPLAN’s Robin Milner Young Researcher Award\, and is a Fellow of the ACM.\n\nHosted by: Professor Mohsen Lesani\n\nLocation: Engineering 2\, Room E2-180\n*Refreshments such as coffee and pastries will be provided.\n\nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1
URL:https://events.ucsc.edu/event/cse-colloquium-flux-refinement-types-for-verified-rust-systems/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251113T100000
DTEND;TZID=America/Los_Angeles:20251113T120000
DTSTAMP:20260425T210153
CREATED:20251110T222658Z
LAST-MODIFIED:20251110T222748Z
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SUMMARY:Petety\, A. (CSE) -  New Algorithmic Methods for Uncertain Inputs
DESCRIPTION:This dissertation focuses on designing and proving performance guarantees on algorithms when there is uncertainty in the input. The uncertainty could be from the user being unsure or future inputs that have not arrived yet. We look at different methods in which algorithms can be designed to be competitive against the optimal. One of the assumptions that helps in this is to assume that the input arrival order is completely random. We study the online load/graph balancing problem when the input arrival order is uniformly random. We show lower bounds for the greedy algorithm and the general case. In the next part\, we study the online scheduling problem under the assumption that the online algorithm has an additional ϵ speed compared to the machines in offline optimal. We show a meta algorithm generalizing Shortest Remaining Processing Time that gives a scalable algorithm for minimizing total weighted flow time. We show that it achieves scalability for minimizing total weighted flow time when the residual optimum exhibits supermodularity. In the final part we look at the online caching problem when the algorithm has access to ML-augmented predictions. We propose an algorithm that achieves a O(logb k) competitive ratio even when using just b predictions per cache miss. We also prove its robustness and consistency. \nEvent Host: Aditya Petety\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Sungjin Im \n 
URL:https://events.ucsc.edu/event/petety-a-cse-new-algorithmic-methods-for-uncertain-inputs/
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251112T110000
DTEND;TZID=America/Los_Angeles:20251112T121500
DTSTAMP:20260425T210153
CREATED:20251106T173342Z
LAST-MODIFIED:20251106T185851Z
UID:10005103-1762945200-1762949700@events.ucsc.edu
SUMMARY:CSE Colloquium - Neurosymbolic AI: from research to industry
DESCRIPTION:Presenter: Luis Lamb\, Catholic Institute of Technology\n\nAbstract:\nNeurosymbolic AI brings together the statistical nature of machine learning with the formal reasoning capabilities of symbolic AI. It seeks to offer a balanced approach to contemporary AI technologies\, by combining the ability to learn from data\, with the capacity to reason upon knowledge acquired from an environment. The main criticism of neural machine learning lies in its lack of explainability and semantics\, which are key requirements in safety-critical applications\, yet inherent strengths of logic-based methods. Recently\, several corporations have publicly announced products and technologies grounded in neurosymbolic AI methodologies. This talk provided a concise review of the foundations\, frameworks and tools underlying neurosymbolic AI\, along with illustrative applications. It concludes by highlighting current trends and research directions in the field.\n\nBio:\nLuis Lamb is Professor of Computer Science and Vice President of Research at the Catholic Institute of Technology. His research interests include: Artificial Intelligence\, Neurosymbolic AI\, Innovation Strategies\, and Applied Logics. Lamb has co-authored two research monographs\, including Neural-Symbolic Cognitive Reasoning\, with d’Avila Garcez and Gabbay (Springer 2009). He organized two Dagstuhl Seminars on Neursymbolic AI\, published widely in AI\, and has worked in the area for over 20 years.  Lamb also has extensive experience leading research planning\, strategy\, and university wide research & infrastructure grant applications\, and strategic academic-industry partnerships. He has been a Professor in Brazil and has experience in industry as a former Senior Manager of AI and Machine Learning at Boeing. He holds a PhD in Computer Science from Imperial College London and an MBA from MIT.\n\nHosted by: Professor Mohsen Lesani\n\nLocation: Engineering 2\, E2-180\n\n*Refreshments such as coffee and pastries will be provided.\n\nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-neurosymbolic-ai-from-research-to-industry/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251107T140000
DTEND;TZID=America/Los_Angeles:20251107T160000
DTSTAMP:20260425T210153
CREATED:20251021T162001Z
LAST-MODIFIED:20251023T212553Z
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SUMMARY:Wang\, S. (CSE) - Learned Hashing and Overlay Networks for AI-native Retrieval and Serving at Scale
DESCRIPTION:Modern AI systems demand low-latency high-quality retrieval and serving over billion-scale keys and vectors. This proposal studies learned hashing and overlay networks to co-locate semantically related items and steer queries with minimal coordination. We first present LEAD\, to our knowledge the first use of order-preserving learned hash functions in distributed key-value overlays\, enabling efficient range queries and cutting hops/messages by 80–90% in prototypes while retaining balance and churn resilience. Second\, Vortex applies learned hashing to approximate nearest-neighbor retrieval: a self-organizing overlay binding learned keys to distributed HNSW indexes to achieve high recall at low fan-out. Third\, PlanetServe introduces onion-style path setup with multi-path dispersal and cache-aware forwarding for open LLM serving\, reducing TTFT and latency while preserving privacy. Planned work generalizes learned hashing to embedding partitions\, token/KV caches\, programmable switches\, and storage tiers\, and provides formal convergence\, load-balancing\, and monotonic-progress guarantees under skew and churn. We are also working to design the first knowledge delivery network for LLM serving: an overlay that unifies data placement\, retrieval\, and policy-aware routing across clusters and providers with tunable cost\, privacy\, and quality. Evaluation on real workloads at scale will measure recall\, tail latency\, cost\, and robustness\, targeting a predictable\, elastic\, scalable AI-native retrieval and serving stack. \nEvent Host: Shengze Wang\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Chen Qian \n  \nZoom: https://ucsc.zoom.us/j/5455463199?pwd=bHRVM01Vd20rcVpkc0FQY01kZG1UUT09&omn=98106984546 \nPasscode: 2121
URL:https://events.ucsc.edu/event/wang-s-cse-learned-hashing-and-overlay-networks-for-ai-native-retrieval-and-serving-at-scale/
CATEGORIES:Ph.D. Presentations
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LOCATION:https://events.ucsc.edu/event/wang-s-cse-learned-hashing-and-overlay-networks-for-ai-native-retrieval-and-serving-at-scale/
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251107T000000
DTEND;TZID=America/Los_Angeles:20251108T235959
DTSTAMP:20260425T210153
CREATED:20251013T212720Z
LAST-MODIFIED:20251023T232623Z
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SUMMARY:United Nations Reboot the Earth Hackathon
DESCRIPTION:The United Nations (UN) and the Baskin School of Engineering at the University of California\, Santa Cruz\, are collaborating to bring the “Reboot the Earth” hackathon to the West Coast for the first time. \nThis is a social event bringing together aspiring developers to create open source software solutions that address the climate crisis\, including wildfire response. It’s a chance to collaborate with peers\, use open data\, and apply your coding skills to real-world climate challenges! \n\n\n\nDate: November 7-8\, 2025\nLocation: UC Santa Cruz Silicon Valley Center.\nRegister here for the event. \n\nOrganized by the UN Office of Information and Communications Technology (OICT)\, the 2025  Reboot the Earth hackathons are focused on agriculture and artificial intelligence (AI). The California event will focus on the locally relevant challenges of wildfire detection\, response\, and impact. Participants can leverage open source\, AI\, and open data sets\, along with local expertise on the environment and emergency preparedness and response. The goal is to build solutions that can become a digital public good\, serving local community needs. \nUC Santa Cruz students interested in attending the event can take advantage of the Silicon Valley Connector shuttle\, which will be running on Saturday\, November 8\, in addition to the regular Friday schedule. \nTo learn more about the Reboot the Earth initiative\, visit: https://unite.un.org/en/reboot-earth.
URL:https://events.ucsc.edu/event/un-reboot-the-earth-hackathon/
LOCATION:Silicon Valley Campus\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States
CATEGORIES:Meetings & Conferences,Social Gathering
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DTSTART;TZID=America/Los_Angeles:20251105T110000
DTEND;TZID=America/Los_Angeles:20251105T121500
DTSTAMP:20260425T210153
CREATED:20251015T215159Z
LAST-MODIFIED:20251022T182643Z
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SUMMARY:CSE Colloquium: Mitigating Data Scarcity via Simulation by Roozbeh Mottaghi
DESCRIPTION:Presenter: Roozbeh Mottaghi\, University of Washington \nAbstract: Data has revolutionized progress across AI fields like natural language processing and computer vision. Yet\, in robotics\, data collection remains a significant challenge: robots must interact with complex\, dynamic environments\, making the process slow\, costly\, and difficult to scale. In this talk\, I will discuss how simulation is transforming the landscape of robotics research by addressing these data bottlenecks. I will introduce Habitat 3.0\, a 3D simulator designed for training and evaluating robotic agents in dynamic environments that include human interactions. Focusing on collaborative human-robot tasks\, I will present PARTNR\, a simulation benchmark designed to rigorously evaluate planning and reasoning in interactive settings. I will share key insights from this benchmark\, revealing both the impressive capabilities of current LLMs and the significant challenges they encounter when faced with the complexities of real-world environments. \nBio: Roozbeh Mottaghi is a Senior Research Scientist Manager at FAIR and an Affiliate Associate Professor in Paul G. Allen School of Computer Science and Engineering at the University of Washington. Prior to joining FAIR\, he was the Research Manager of the Perceptual Reasoning and Interaction Research (PRIOR) group at the Allen Institute for AI (AI2). He obtained his PhD in Computer Science in 2013 from the University of California\, Los Angeles. After PhD\, he joined the Computer Science Department at Stanford University as a post-doctoral researcher. His research mainly focuses on embodied AI\, reasoning via perception\, and learning via interaction\, and his work on large-scale Embodied AI received the Outstanding Paper Award at NeurIPS 2022. \n\n\n\n\n\n\nFaculty Host: Professor Mohsen Lesani \n\nLocation: Engineering 2\, E2-180\n\n*Refreshments such as coffee and pastries will be provided.\n\nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-mitigating-data-scarcity-via-simulation-by-roozbeh-mottaghi/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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